vardpoor (version 0.6.2)

vardcrospoor: Variance estimation for cross-sectional, longitudinal measures for indicators on social exclusion and poverty.

Description

Computes the variance estimation for cross-sectional and longitudinal measures for indicators on social exclusion and poverty.

Usage

vardcrospoor(Y, 
                     age=NULL,
                     pl085 = NULL,
                     month_at_work = NULL,
                     Y_den=NULL,
                     Y_thres = NULL,
                     wght_thres = NULL, 
                     H, PSU, w_final, id,
                     Dom = NULL,
                     country, periods,
                     sort=NULL,
                     gender = NULL,
                     percentage=60,
                     order_quant=50,
                     alpha = 20,
                     dataset = NULL,
                     use.estVar = FALSE,
                     withperiod = TRUE,
                     netchanges = TRUE,
                     confidence = .95,
                     type="linrmpg")

Arguments

Y

Variables of interest. Object convertible to data.table or variable names as character, column numbers.

age

Age variable. One dimensional object convertible to one-column data.table or variable name as character, column number.

pl085

Retirement variable (Number of months spent in retirement or early retirement). One dimensional object convertible to one-column data.table or variable name as character, column number.

month_at_work

Variable for total number of month at work (sum of the number of months spent at full-time work as employee, number of months spent at part-time work as employee, number of months spent at full-time work as self-employed (including family worker), number

Y_den

Denominator variable (for example gross individual earnings). One dimensional object convertible to one-column data.table or variable name as character, column number.

Y_thres

Variable (for example equalized disposable income) used for computation and linearization of poverty threshold. One dimensional object convertible to one-column data.table or variable name as character, column number or logical vector with on

wght_thres

Weight variable used for computation and linearization of poverty threshold. One dimensional object convertible to one-column data.table or variable name as character, column number. Variable specified for weight is used as

H

The unit stratum variable. One dimensional object convertible to one-column data.table or variable name as character, column number.

PSU

Primary sampling unit variable. One dimensional object convertible to one-column data.table or variable name as character, column number.

w_final

Weight variable. One dimensional object convertible to one-column data.table or variable name as character, column number.

id

variable for unit ID codes (for household surveys - secondary unit id number). One dimensional object convertible to one-column data.table or variable name as character, column number.

Dom

Optional variables used to define population domains. If supplied, variables are calculated for each domain. An object convertible to data.table or variable names as character vector, column numbers.

country

Variable for the survey countries. The values for each country are computed independently. Object convertible to data.table or variable names as character, column numbers.

periods

Variable for the survey periods. The values for each period are computed independently. Object convertible to data.table or variable names as character, column numbers.

sort

Optional variable to be used as tie-breaker for sorting. One dimensional object convertible to one-column data.table or variable name as character, column number.

gender

Numerical variable for gender, where 1 is for males, but 2 is for females. One dimensional object convertible to one-column data.table or variable name as character, column number.

percentage

A numeric value in range $[0,100]$ for $p$ in the formula for poverty threshold computation: $$\frac{p}{100} \cdot Z_{\frac{\alpha}{100}}.$$ For example, to compute poverty threshold equal to 60% of some income quantile, $p$ should be set equal to 60.

order_quant

A numeric value in range $[0,100]$ for $\alpha$ in the formula for poverty threshold computation: $$\frac{p}{100} \cdot Z_{\frac{\alpha}{100}}.$$ For example, to compute poverty threshold equal to some percentage of median income, $\alpha$ should be set

alpha

a numeric value in range $[0,100]$ for the order of the income quantile share ratio (in percentage).

dataset

Optional survey data object convertible to data.table.

use.estVar

Logical value. If value is TRUE, then R function estVar is used for the estimation of covariance matrix of the residuals. If value is FALSE, then R function estVar is not used

withperiod

Logical value. If TRUE is value, the results is with period, if FALSE, without period.

netchanges

Logical value. If value is TRUE, then produce two objects: the first object is aggregation of weighted data by period (if available), country, strata and PSU, the second object is an estimation for Y, the variance, gradient for numerator and denominator b

confidence

Optional positive value for confidence interval. This variable by default is 0.95.

type

a character vector (of length one unless several.ok is TRUE), example "linarpr","linarpt", "lingpg", "linpoormed", "linrmpg", "lingini", "lingini2", "linqsr", "linarr", "linrmir", "all_choices".

Value

• A list with two objects are returned by the function:
• data_net_changes
  A data.table containing aggregation of weighted data by period (if available), country, strata, PSU.
• results
  A data.table containing period - survey periods, country - survey countries, Dom - optional variable of the population domains, type - type variable, count_respondents - the count of respondents, pop_size - the population size (in numbers of individuals), estim - the estimated value, se - the estimated standard error, var - the estimated variance, rse - the estimated relative standard error (coefficient of variation), cv - the estimated relative standard error (coefficient of variation) in percentage.

References

Guillaume Osier, Yves Berger, Tim Goedeme, (2013), Standard error estimation for the EU-SILC indicators of poverty and social exclusion, Eurostat Methodologies and Working papers, URL http://ec.europa.eu/eurostat/documents/3888793/5855973/KS-RA-13-024-EN.PDF. Yves G. Berger, Tim Goedeme, Guillame Osier (2013). Handbook on standard error estimation and other related sampling issues in EU-SILC, URL https://ec.europa.eu/eurostat/cros/content/handbook-standard-error-estimation-and-other-related-sampling-issues-ver-29072013_en Eurostat Methodologies and Working papers, Handbook on precision requirements and variance estimation for ESS household surveys, 2013, URL http://ec.europa.eu/eurostat/documents/3859598/5927001/KS-RA-13-029-EN.PDF.

See Also

linrmir, linarr, vardchanges

Examples

data(eusilc)
set.seed(1)

data <- data.table(rbind(eusilc, eusilc),
                   year=c(rep(2010, nrow(eusilc)),
                          rep(2011, nrow(eusilc))),
                   country=c(rep("AT", nrow(eusilc)),
                             rep("AT", nrow(eusilc))))
data[age<0, age:=0]
PSU <- data[,.N, keyby="db030"]
PSU[, N:=NULL]
PSU[, PSU:=trunc(runif(nrow(PSU), 0, 100))]
PSU$inc <- runif(nrow(PSU), 20, 100000)
data <- merge(data, PSU, all=TRUE, by="db030")
PSU <- eusilc <- NULL
data[, strata:=c("XXXX")]
data[, strata:=as.character(strata)]
data$pl085 <- 12*trunc(runif(nrow(data),0,2))
data$month_at_work <- 12*trunc(runif(nrow(data),0,2))

result <- vardcrospoor(Y="inc", age="age",
                     pl085="pl085", 
                     month_at_work="month_at_work",
                     Y_den="inc", Y_thres="inc",
                     wght_thres="rb050",
                     H="strata", PSU="PSU", 
                     w_final="rb050",  id=NULL,
                     Dom = c("rb090", "db040"),
                     country="country", periods="year",
                     sort=NULL,
                     gender = NULL,
                     percentage=60,
                     order_quant=50,
                     alpha = 20,
                     dataset = data,
                     confidence=0.95,
                     type="linrmpg")

result2 <- vardcrospoor(Y="inc",  age="age",
                     pl085="pl085", 
                     month_at_work="month_at_work",
                     Y_den="inc", Y_thres="inc",
                     wght_thres="rb050",
                     H="strata", PSU="PSU", 
                     w_final="rb050", id=NULL,
                     Dom = c("rb090", "db040"),
                     country="country", periods="year",
                     sort=NULL,
                     gender = NULL,
                     percentage=60,
                     order_quant=50,
                     alpha = 20,
                     dataset = data,
                     confidence=0.95,
                     type="linrmpg")